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Joint Training for Neural Machine Translation

monolingual corpora, so as to incorporate these corpora into neural machine translation. It then introduces a joint training algorithm for pivot-based;

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Neural Machine Translation

language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation;

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The Human Factor in Machine Translation

Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online;

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The Human Factor in Machine Translation

Machine translation has become increasingly popular, especially with the introduction of neural machine translation in major online;

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Machine Translation

of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.;

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Machine Translation

of machine translation, including preprocessing, neural machine translation models, hybrid model, evaluation method, and post-editing.;

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TensorFlow 2.0 Quick Start Guide

of machine learning and neural network TensorFlow techniques. What you will learn Use tf.Keras for fast prototyping, building, and training deep;

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Evolutionary Machine Learning Techniques

, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons;

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Evolutionary Machine Learning Techniques

, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the;

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Neural Network Methods in Natural Language Processing

architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other;

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Neural Network Methods in Natural Language Processing

architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other;

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Computational Learning Theory & Natural Learning Systems V 2 - Intersections Between Theory & Experiment

This second volume defines possible areas of joint research in artificial intelligence and machine learning. It uses knowledge from the;

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Hands-On Machine Learning with Scikit-Learn and TensorFlow

, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply;

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Focus on Artificial Neural Networks

of pharamceutical microemulsions; massive-training artificial neural networks for supervised enhancement/suppression of lesions/patterns in medical images;

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Statistical Machine Translation

explains how to build machine translation systems for any two languages. It introduces the subject's building blocks from linguistics and;

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Deep Learning Quick Reference

Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's;

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Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets;

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Practical TensorFlow.js

learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural;

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Practical TensorFlow.js

learning products suitable for web browsers Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks;

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TensorFlow For Dummies

translation, robotics, handwriting analysis, financial prediction, and image recognition. TensorFlow is Google's preeminent toolset for machine;

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Deep Natural Language Processing and AI Applications for Industry 5.0

intelligence is applicable either by simplifying the communication process or by refining and analyzing information. Neural machine translation has;

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Introduction to Machine Learning

. The fourth edition offers a new chapter on deep learning that discusses training, regularizing, and structuring deep neural networks such as;

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Hands-On Convolutional Neural Networks with TensorFlow

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for;

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Domain Adaptation for Visual Understanding

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding;

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Domain Adaptation for Visual Understanding

This unique volume reviews the latest advances in domain adaptation in the training of machine learning algorithms for visual understanding;

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Recurrent Neural Networks with Python Quick Start Guide

Learn how to develop intelligent applications with sequential learning and apply modern methods for language modeling with neural network;

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Learning Machine Translation

discriminative training framework for leveraging syntactic information, the use of semi-supervised and kernel-based learning methods, and the combination;

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